Commentary generation

ABSTRACT

One or more computing devices, systems, and/or methods for commentary generation are provided. For example, a conversation, occurring through a conversation interface associated with a content item, is monitored to identify a tone of the conversation (e.g., users discussing a news article). If the tone deviates from a target tone (e.g., a negative tone of inflammatory comments, a low participation tone, an off topic tone, etc.), then intervention is automatically and programmatically performed for the conversation. For example, subject matter of the content item, information from external sources (e.g., other articles, social network posts, or website content associated with a topic of the news article), and/or programmatically generated information (e.g., topical statements generated by a neural network) are used to construct a comment. The comment is posted to the conversation interface in order to improve the conversation, such as to increase positive engagement by users.

CROSS REFERENCE TO RELATED APPLICATION

This application claims priority to and is a continuation of U.S.application Ser. No. 15/359,994, filed on Nov. 23, 2016, entitled“COMMENTARY GENERATION”, which is incorporated herein.

BACKGROUND

Content, such as a video, a live stream, an e-sporting event, anarticle, an image, and/or other types of content, may be distributed tomultiple users concurrently. For example, thousands of users may watch alive e-sporting event pertaining to a soccer game. Users may desire toshare their opinions, thoughts, and/or reactions while viewing thecontent. In an example, a conversation interface (e.g., instances ofuser chat interfaces, of a chat room, provided to computing devices ofusers accessing the content) may be provided to the users while viewingthe content. In this way, the users may share messages with one anotherthrough the chat room.

Unfortunately, a tone of a conversation within the chat room may becomenegative (e.g., a user may start to post comments that are inflammatory,derogatory, etc.), off topic, and/or experience times of lowparticipation. If the tone of the conversation becomes negative,off-topic, and/or quiet, then users may leave the chat room and/or comeaway with a negative experience. Thus, computing resources and/orbandwidth used to host the chat room are wasted. Manual attempts byhuman moderators to identity undesirable conversations and intervene maybe costly and cannot scale to large conversations and/or services thathost a large amount of content items for which conversation interfacesare provided (e.g., a news website may provide conversation interfacesfor hundreds or thousands of articles).

SUMMARY

In accordance with the present disclosure, one or more computing devicesand/or methods for commentary generation are provided. For example, aconversation interface (e.g., instances of chat room interfaces,provided to computing devices of users, hosting a chat room throughwhich users can submit comments as a conversation) may be associatedwith a content item, such as an article, a video, a live stream, animage, and/or other content that users are accessing. The users canshare comments through the conversation interface in order to engage ina conversation relating to the content item. The conversation may bemonitored to identify a tone of the conversation based upon commentssubmitted by users through the conversation interface (e.g., an abusivelanguage detector may be used to detect inflammatory comments indicativeof a negative tone for the conversation).

Responsive to determining that the tone deviates from a target tone(e.g., an identification of a threshold number of inflammatory comments,a lack of comments indicating lower user participation, off topiccomments, etc.), a subject matter of the content item may be identified.For example, text of a topic (e.g., a title, metadata describing thecontent item, labels or tags describing the content item, etc.) and/orbody of an article (e.g., text of the article, subject matter depictedby an image within the article recognizable by image and featurerecognition, etc.) may be evaluated by a subject matter classifier toidentify the subject matter (e.g., the article may related to a newstrategy videogame).

In one example, a natural language statement may be generated based uponthe subject matter. For example, a question may be generated based upontext of the topic and/or the body of the article (e.g., “how do you likethe third level of the game?”). In this way, the natural languagestatement may be posted as a comment through the conversation interface.

In another example, a content source (e.g., a social network, an onlineencyclopedia, a question and answer service, a blogging website, aforum, etc.) may be queried to identify target content corresponding tothe subject matter (e.g., a second article about newly releasedvideogames). A snippet may be extracted from the target content basedupon a selection criteria (e.g., a natural language selection criteria,a uniqueness selection criteria, a responsiveness selection criteria, acontextual selection criteria, etc.). In this, way the snippet may beselected as text that reads as a natural language statement, text thatis not redundant with other comments of the conversation, text that mayimprove user engagement with the conversation, and/or text that iscontextually relevant to the conversation and/or the content item. Acomment may be generated based upon the snippet. The comment may beposted through the conversation interface.

In another example, a comment generator model (e.g., a neural networktrained to generate text that is topical for the subject matter) may beused to construct a comment based upon the subject matter. In anexample, a matrix of user interests associated with users of theconversation may be used by a factorization machine of the commentgenerator model to generate the comment that may be interesting and/orrelevant to users. The comment may be posted through the conversationinterface.

The comment may be tailored as intervention for the conversation inorder to help steer the conversation in a more positive, productive,and/or interaction conversation.

DESCRIPTION OF THE DRAWINGS

While the techniques presented herein may be embodied in alternativeforms, the particular embodiments illustrated in the drawings are only afew examples that are supplemental of the description provided herein.These embodiments are not to be interpreted in a limiting manner, suchas limiting the claims appended hereto.

FIG. 1 is an illustration of a scenario involving various examples ofnetworks that may connect servers and clients.

FIG. 2 is an illustration of a scenario involving an exampleconfiguration of a server that may utilize and/or implement at least aportion of the techniques presented herein.

FIG. 3 is an illustration of a scenario involving an exampleconfiguration of a client that may utilize and/or implement at least aportion of the techniques presented herein.

FIG. 4 is a flow chart illustrating an example method for commentarygeneration.

FIG. 5A is a component block diagram illustrating an example system forcommentary generation, where a tone of a conversation is identified.

FIG. 5B is a component block diagram illustrating an example system forcommentary generation, where a comment is posted to a conversationinterface.

FIG. 6A is a component block diagram illustrating an example system forcommentary generation, where a tone of a conversation is identified.

FIG. 6B is a component block diagram illustrating an example system forcommentary generation, where a comment is posted to a conversationinterface.

FIG. 7A is a component block diagram illustrating an example system forcommentary generation, where a tone of a conversation is identified.

FIG. 7B is a component block diagram illustrating an example system forcommentary generation, where a comment is posted to a conversationinterface.

FIG. 8 is an illustration of a scenario featuring an examplenon-transitory machine readable medium in accordance with one or more ofthe provisions set forth herein.

DETAILED DESCRIPTION

Subject matter will now be described more fully hereinafter withreference to the accompanying drawings, which form a part hereof, andwhich show, by way of illustration, specific example embodiments. Thisdescription is not intended as an extensive or detailed discussion ofknown concepts. Details that are known generally to those of ordinaryskill in the relevant art may have been omitted, or may be handled insummary fashion.

The following subject matter may be embodied in a variety of differentforms, such as methods, devices, components, and/or systems.Accordingly, this subject matter is not intended to be construed aslimited to any example embodiments set forth herein. Rather, exampleembodiments are provided merely to be illustrative. Such embodimentsmay, for example, take the form of hardware, software, firmware or anycombination thereof.

1. Computing Scenario

The following provides a discussion of some types of computing scenariosin which the disclosed subject matter may be utilized and/orimplemented.

1.1. Networking

FIG. 1 is an interaction diagram of a scenario 100 illustrating aservice 102 provided by a set of servers 104 to a set of client devices110 via various types of networks. The servers 104 and/or client devices110 may be capable of transmitting, receiving, processing, and/orstoring many types of signals, such as in memory as physical memorystates.

The servers 104 of the service 102 may be internally connected via alocal area network 106 (LAN), such as a wired network where networkadapters on the respective servers 104 are interconnected via cables(e.g., coaxial and/or fiber optic cabling), and may be connected invarious topologies (e.g., buses, token rings, meshes, and/or trees). Theservers 104 may be interconnected directly, or through one or more othernetworking devices, such as routers, switches, and/or repeaters. Theservers 104 may utilize a variety of physical networking protocols(e.g., Ethernet and/or Fiber Channel) and/or logical networkingprotocols (e.g., variants of an Internet

Protocol (IP), a Transmission Control Protocol (TCP), and/or a UserDatagram Protocol (UDP). The local area network 106 may include, e.g.,analog telephone lines, such as a twisted wire pair, a coaxial cable,full or fractional digital lines including T1, T2, T3, or T4 type lines,Integrated Services Digital Networks (ISDNs), Digital Subscriber Lines(DSLs), wireless links including satellite links, or other communicationlinks or channels, such as may be known to those skilled in the art. Thelocal area network 106 may be organized according to one or more networkarchitectures, such as server/client, peer-to-peer, and/or mesharchitectures, and/or a variety of roles, such as administrativeservers, authentication servers, security monitor servers, data storesfor objects such as files and databases, business logic servers, timesynchronization servers, and/or front-end servers providing auser-facing interface for the service 102.

Likewise, the local area network 106 may comprise one or moresub-networks, such as may employ differing architectures, may becompliant or compatible with differing protocols and/or may interoperatewithin the local area network 106. Additionally, a variety of local areanetworks 106 may be interconnected; e.g., a router may provide a linkbetween otherwise separate and independent local area networks 106.

In the scenario 100 of FIG. 1, the local area network 106 of the service102 is connected to a wide area network 108 (WAN) that allows theservice 102 to exchange data with other services 102 and/or clientdevices 110. The wide area network 108 may encompass variouscombinations of devices with varying levels of distribution andexposure, such as a public wide-area network (e.g., the Internet) and/ora private network (e.g., a virtual private network (VPN) of adistributed enterprise).

In the scenario 100 of FIG. 1, the service 102 may be accessed via thewide area network 108 by a user 112 of one or more client devices 110,such as a portable media player (e.g., an electronic text reader, anaudio device, or a portable gaming, exercise, or navigation device); aportable communication device (e.g., a camera, a phone, a wearable or atext chatting device); a workstation; and/or a laptop form factorcomputer. The respective client devices 110 may communicate with theservice 102 via various connections to the wide area network 108. As afirst such example, one or more client devices 110 may comprise acellular communicator and may communicate with the service 102 byconnecting to the wide area network 108 via a wireless local areanetwork 106 provided by a cellular provider. As a second such example,one or more client devices 110 may communicate with the service 102 byconnecting to the wide area network 108 via a wireless local areanetwork 106 provided by a location such as the user's home or workplace(e.g., a WiFi (Institute of Electrical and Electronics Engineers (IEEE)Standard 802.11) network or a Bluetooth (IEEE Standard 802.15.1)personal area network). In this manner, the servers 104 and the clientdevices 110 may communicate over various types of networks. Other typesof networks that may be accessed by the servers 104 and/or clientdevices 110 include mass storage, such as network attached storage(NAS), a storage area network (SAN), or other forms of computer ormachine readable media.

1.2. Server Configuration

FIG. 2 presents a schematic architecture diagram 200 of a server 104that may utilize at least a portion of the techniques provided herein.Such a server 104 may vary widely in configuration or capabilities,alone or in conjunction with other servers, in order to provide aservice such as the service 102.

The server 104 may comprise one or more processors 210 that processinstructions. The one or more processors 210 may optionally include aplurality of cores; one or more coprocessors, such as a mathematicscoprocessor or an integrated graphical processing unit (GPU); and/or oneor more layers of local cache memory. The server 104 may comprise memory202 storing various forms of applications, such as an operating system204; one or more server applications 206, such as a hypertext transportprotocol (HTTP) server, a file transfer protocol (FTP) server, or asimple mail transport protocol (SMTP) server; and/or various forms ofdata, such as a database 208 or a file system. The server 104 maycomprise a variety of peripheral components, such as a wired and/orwireless network adapter 214 connectible to a local area network and/orwide area network; one or more storage components 216, such as a harddisk drive, a solid-state storage device (SSD), a flash memory device,and/or a magnetic and/or optical disk reader.

The server 104 may comprise a mainboard featuring one or morecommunication buses 212 that interconnect the processor 210, the memory202, and various peripherals, using a variety of bus technologies, suchas a variant of a serial or parallel AT Attachment (ATA) bus protocol; aUniform Serial Bus (USB) protocol; and/or Small Computer SystemInterface (SCI) bus protocol. In a multibus scenario, a communicationbus 212 may interconnect the server 104 with at least one other server.Other components that may optionally be included with the server 104(though not shown in the schematic architecture diagram 200 of FIG. 2)include a display; a display adapter, such as a graphical processingunit (GPU); input peripherals, such as a keyboard and/or mouse; and aflash memory device that may store a basic input/output system (BIOS)routine that facilitates booting the server 104 to a state of readiness.

The server 104 may operate in various physical enclosures, such as adesktop or tower, and/or may be integrated with a display as an“all-in-one” device. The server 104 may be mounted horizontally and/orin a cabinet or rack, and/or may simply comprise an interconnected setof components. The server 104 may comprise a dedicated and/or sharedpower supply 218 that supplies and/or regulates power for the othercomponents. The server 104 may provide power to and/or receive powerfrom another server and/or other devices. The server 104 may comprise ashared and/or dedicated climate control unit 220 that regulates climateproperties, such as temperature, humidity, and/or airflow. Many suchservers 104 may be configured and/or adapted to utilize at least aportion of the techniques presented herein.

1.3. Client Device Configuration

FIG. 3 presents a schematic architecture diagram 300 of a client device110 whereupon at least a portion of the techniques presented herein maybe implemented. Such a client device 110 may vary widely inconfiguration or capabilities, in order to provide a variety offunctionality to a user such as the user 112. The client device 110 maybe provided in a variety of form factors, such as a desktop or towerworkstation; an “all-in-one” device integrated with a display 308; alaptop, tablet, convertible tablet, or palmtop device; a wearable devicemountable in a headset, eyeglass, earpiece, and/or wristwatch, and/orintegrated with an article of clothing; and/or a component of a piece offurniture, such as a tabletop, and/or of another device, such as avehicle or residence. The client device 110 may serve the user in avariety of roles, such as a workstation, kiosk, media player, gamingdevice, and/or appliance.

The client device 110 may comprise one or more processors 310 thatprocess instructions. The one or more processors 310 may optionallyinclude a plurality of cores; one or more coprocessors, such as amathematics coprocessor or an integrated graphical processing unit(GPU); and/or one or more layers of local cache memory. The clientdevice 110 may comprise memory 301 storing various forms ofapplications, such as an operating system 303; one or more userapplications 302, such as document applications, media applications,file and/or data access applications, communication applications such asweb browsers and/or email clients, utilities, and/or games; and/ordrivers for various peripherals. The client device 110 may comprise avariety of peripheral components, such as a wired and/or wirelessnetwork adapter 306 connectible to a local area network and/or wide areanetwork; one or more output components, such as a display 308 coupledwith a display adapter (optionally including a graphical processing unit(GPU)), a sound adapter coupled with a speaker, and/or a printer; inputdevices for receiving input from the user, such as a keyboard 311, amouse, a microphone, a camera, and/or a touch-sensitive component of thedisplay 308; and/or environmental sensors, such as a global positioningsystem (GPS) receiver 319 that detects the location, velocity, and/oracceleration of the client device 110, a compass, accelerometer, and/orgyroscope that detects a physical orientation of the client device 110.Other components that may optionally be included with the client device110 (though not shown in the schematic architecture diagram 300 of FIG.3) include one or more storage components, such as a hard disk drive, asolid-state storage device (SSD), a flash memory device, and/or amagnetic and/or optical disk reader; and/or a flash memory device thatmay store a basic input/output system (BIOS) routine that facilitatesbooting the client device 110 to a state of readiness; and a climatecontrol unit that regulates climate properties, such as temperature,humidity, and airflow.

The client device 110 may comprise a mainboard featuring one or morecommunication buses 312 that interconnect the processor 310, the memory301, and various peripherals, using a variety of bus technologies, suchas a variant of a serial or parallel AT Attachment (ATA) bus protocol;the Uniform Serial Bus (USB) protocol; and/or the Small Computer SystemInterface (SCI) bus protocol. The client device 110 may comprise adedicated and/or shared power supply 318 that supplies and/or regulatespower for other components, and/or a battery 304 that stores power foruse while the client device 110 is not connected to a power source viathe power supply 318. The client device 110 may provide power to and/orreceive power from other client devices.

In some scenarios, as a user 112 interacts with a software applicationon a client device 110 (e.g., an instant messenger and/or electronicmail application), descriptive content in the form of signals or storedphysical states within memory (e.g., an email address, instant messengeridentifier, phone number, postal address, message content, date, and/ortime) may be identified. Descriptive content may be stored, typicallyalong with contextual content. For example, the source of a phone number(e.g., a communication received from another user via an instantmessenger application) may be stored as contextual content associatedwith the phone number. Contextual content, therefore, may identifycircumstances surrounding receipt of a phone number (e.g., the date ortime that the phone number was received), and may be associated withdescriptive content. Contextual content, may, for example, be used tosubsequently search for associated descriptive content. For example, asearch for phone numbers received from specific individuals, receivedvia an instant messenger application or at a given date or time, may beinitiated. The client device 110 may include one or more servers thatmay locally serve the client device 110 and/or other client devices ofthe user 112 and/or other individuals. For example, a locally installedwebserver may provide web content in response to locally submitted webrequests. Many such client devices 110 may be configured and/or adaptedto utilize at least a portion of the techniques presented herein.

2. Presented Techniques

One or more computing devices and/or techniques for commentarygeneration are provided. For example, users may interact with aconversation interface associated with a content item, such as chatinterfaces displayed through computing devices of users watching apolitical debate. The users may post comments through the conversationinterface in order to share their opinions and/or thoughts.Unfortunately, negative, inflammatory, and/or off topic comments may beposted. Such comments and/or low user participation may diminish or ruinthe conversation for users. Thus, the users may leave the conversation,and thus computing resources used to host the conversation interface maybe wasted. Manually monitoring conversations of conversation interfacesmay expend a significant amount of time and computer resources forhumans to manually intervene (e.g., each human monitor may require acomputing device, computing resources, and/or network bandwidth formanually monitoring conversations). Such manual intervention does notscale for services that host hundreds or thousands of conversations forvarious content items.

Accordingly, as provided herein, the conversation may be monitored(e.g., by a computer bot) in order to automatically and/orprogrammatically intervene when a tone of the conversation becomesundesirable, such as where inflammatory and/or negative comments arebeing posted to the conversation. Various techniques are provided forautomatically and/or programmatically generating comments that are usedto steer the conversation towards a more desirable tone, such as byposting questions that will increase positive user engagement with theconversation. In this way, comments may be programmatically generatedand posted to the conversation in order to improve the conversationwithout manual intervention that otherwise wastes time and/or computingresources for humans to manually monitor and intervene withconversations.

An embodiment of commentary generation is illustrated by an examplemethod 400 of FIG. 4. A content provider may provide users with accessto content items, such as a videogame review article. The contentprovider or another service may provide users with access to aconversation interface through which users can discuss the videogamereview article (e.g., instances of a chat interface may be displaythrough computing devices of users accessing the videogame reviewarticle). Unfortunately, a tone of a conversation of the conversationinterface may become negative with inflammatory comments (e.g., the useof abusive or inflammatory language), off topic (e.g., two users maystart to state their political options), and/or inactive (e.g., a lownumber of users are submitted comments).

Accordingly, as provided herein, automatic and programmatic computermonitoring of the conversation occurring through the conversationinterface may be provided for identifying the tone of the conversationbased upon comments submitted by users through the conversationinterface, at 402. For example, text, images, icons, emoticons, and/orother user generated content provided through comments may be evaluated(e.g., using text recognition functionality, image recognitionfunctionality, feature recognition functionality, speech recognitionfunctionality, topic classifiers, keyword matching functionality, etc.)to identify a undesirable tone for the conversation based upon athreshold number of comments having inflammatory words, off topicstatements, and/or a lack of comments.

The tone may be evaluated to determine whether the tone deviates from adesired target tone. For example, the tone may deviate from the targettone if the tone corresponds to a negative conversation tone, an offtopic conversation tone, or a silent conversation tone (e.g., a lowparticipation conversation tone). At 404, responsive to determining thatthe tone deviates from the desired target tone, a subject matter of thecontent item may be identified. In an example, a topic (e.g., a header,a title, tagged metadata, user comments, user ratings, etc.) and/or abody (e.g., text and/or images of the videogame review article) may beextracted and evaluated to identify the subject matter of the contentitem. For example, the subject matter may indicate that the videogamereview article corresponds to a race car videogame for a videogamesystem (X) that has received a rating of 8/10 from a videogame reviewwebsite. Various subject classifiers, topic classifiers, featuresclassifiers, entity classifiers, and/or other classifiers and/orrecognition functionality/algorithms may be used to identify the subjectmatter.

At 406, a comment may be automatically and programmatically computergenerated and posted through the conversation interface as anintervention for the conversation in order to increase user engagementin useful, positive, engaging, and/or interactive ways.

In one embodiment of generating the comment, a natural languagestatement may be generated based upon the subject matter. Words from thetopic and/or the body of the videogame review article may be utilized togenerate the comment, such as a question generated formed from thewords. For example, a natural language statement generator may utilizethe words to construct the natural language statement as the comment(e.g., “I just bought the videogame system (X), when did everyone elsebuy it?”). In an example, a structured knowledge content source (e.g., asocial network, a microblogging service, a forum, an onlineencyclopedia, a website, etc.) may be searched using the subject matterof the videogame review article to identify structured knowledge (e.g.,a release date for the race car videogame) associated with the contentitem. The natural language statement may be generated to comprise anartificially generated fact derived from the structured knowledge (e.g.,“Is anyone else excited about the upcoming release date of thisFriday?”). In this way, the natural language statement may be posted asthe comment through the conversation interface.

In one embodiment of generating the comment, a content source (e.g., aquestion and answer website, a social network, a microblogging service,a forum, an online encyclopedia, a website, etc.) may be queried toidentify target content corresponding to the subject matter. Forexample, a second videogame website may be searched to identify astrategy guide article for the race car videogame. A snippet may beextracted from the target content based upon a selection criteria. Forexample, the snippet may comprise text from the target content such as“Driver (X) is the hardest opponent”.

The snippet may be selected based upon various selection criteria. In anexample, a natural language selection criteria may be used to assignweights to snippets (e.g., phrases or sentences) within the strategyguide article based upon how much each snippet corresponds to naturallanguage statements (e.g., a snippet “Up, Up, Down, Down, Left, Right,Left, Right, B, A, Start” may be assigned a lower weight because thesnippet does not correspond to a natural language statement, whereas thesnippet “Driver (X) is the hardest opponent” may be assigned a higherweight because the snippet corresponds more to a natural languagestatement.

In another example, a uniqueness selection criteria may be used toassign weights to snippets based upon how unique content of each snippetis from comments of the conversation (e.g., recent comments postedwithin a threshold timespan such as a last 10 minutes of comments). Theuniqueness selection criteria is used to avoid the generation ofredundant comments that may be less useful in changing the tone of theconversation compared to unique comments. For example, lower weights maybe assigned to snippets that have more words matching words of recentcomments within the conversation than snippets that do not have wordsmatching the recent comments within the conversation.

In another example, a responsiveness selection criteria may be used toassign weights to snippets based upon determined probabilities thatsnippets will elicit users to submit reply comments to the snippets(e.g., a probability of reengaging users into the conversation). Forexample, a snippet “Videogame Company (X) made videogame system (X)” maybe assigned a lower weight because less users are likely to respond tosuch a statement, whereas a snippet “the three best race cars are car(A), car (B), and car (D)” that may be more likely to elicit other usersto submit their opinions on which race cars are the best. Various typesof classifiers may be used to evaluate and assign weights based uponresponsiveness.

In another example, a contextual selection criteria is used to assignweights to snippets based upon contextual relevancy of each snippet tothe content item. For example, snippets that have topics and/or wordsthat correlate to the content item (e.g., text of the topic and/or thebody of the content item) and/or the subject matter of the content itemmay be assigned higher weights than snippets that do not have topicsand/or words matching such.

In this way, a snippet may be selected based upon the snippet having adesired weight (e.g., a highest weight). A comment may be generatedbased upon the snippet. In an example, the snippet may be used word forword (e.g., “Driver (X) is the hardest opponent”) as the comment. Forexample, the snippet may be utilized as a quote for the target content.The quote and a citation to the target content may be included withinthe comment. In another example, the snippet may be modified by adding,removing, or changing words (e.g., “Drive (X) is the hardest opponent,what do you think?”).

In one embodiment of generating the comment, a comment generator modelmay be used to construct the comment, such as a question, based upon thesubject matter. In an example, the comment generator model comprises aneural network configured to generate text that is topical for thesubject matter of the content item. The neural network may have beentrained using training comments that were labeled as positive comments(e.g., comments that received numerous positive user replies, commentsthat do not comprise inflammatory statements, comments that numeroususers liked, comments that led to an interactive discussion that stayedon topic and did not have abusive or inflammatory comments, etc.). Forexample, a training comment is labeled as the positive comment basedupon a threshold number of approval ratings being assigned to thetraining comment by users (e.g., users “liking” the training commentthrough a forum, social network, chat interface, blogging service,website, etc.). In an example, the neural network may also be trained todetermine how a negative comment is structured so that the neuralnetwork can avoid generating negative comments for posting within theconversation interface.

In an example, a context vector may be generated for the content itembased upon the subject matter. For example, the context vector maycomprise words extracted from the content item (e.g., a vector of thefirst fifty words occurring within the body of the race car videogamearticle). The comment generator model may be utilized to construct thecomment based upon the context vector, such as by constructing thecomment using words within the context vector.

In an example, user signals (e.g., a user profile, social network postsby users, email content of an email account of a user, calendar eventsof a user calendar, previous comments posted by the user for theconversation or past conversations, browsing history of the user, etc.)may be evaluated to identify user interests of users participating inthe conversation. It may be appreciated that users may provide opt-inconsent for the use of the user signals, such as for the purpose ofcomment generation. A matrix of user interests, associated with users ofthe conversation interface, may be generated. A factorization machine,of the comment generator model, may be used to generate the commentbased upon the matrix of user interests. In this way, the comment may beformulated with statements that may be interesting and/or relevant tointerests of users of the conversation interface.

In an example, the comment generator model may be utilized to generatethe comment based upon content of user comments by users of theconversation interface. For example, the comment may be generated usingwords of a user comment by a user that has left the conversationinterface. In an example, the user may be considered based upon the userhaving left the conversation over a threshold amount of time from acurrent time (e.g., users that have left the conversation over 10minutes prior to the current time). In another example, the user may beconsidered based upon the user having left the conversation interfaceand a last comment by the user being posted over a threshold amount oftime from a current time. In another example, the user may be consideredbased upon the user having left the conversation interface and anoccurrence of a threshold number of comments being posted since a mostrecent post by the user (e.g., over 15 comments have occurred since theuser left). In this way, users of the conversation interface may notrealize that the comment was derived from a prior comment, and thus maybe seen as a fresh comment that could elicit user engagement and/orre-engagement.

In an example, a user comment of a user, user identifying information ofthe user (e.g., the user signals, a user interest derived from the usersignals, etc.), and/or co-commentary behavior of users with the user maybe utilized to predict a likelihood of the user re-engaging in theconversation based upon a potential comment, generated by the commentgenerator model, being posted to the conversation interface. Forexample, a user interest in race car tires and co-commentary behaviorindicating that the user is very engaged with other users whendiscussing types of race car tires may be used to determine that apotential comment about race car tire types available for the race carvideogame has a high likelihood of getting the user to re-engage withthe conversation. Responsive to the likelihood exceeding a threshold,the potential comment may be posted as the comment within theconversation interface.

FIGS. 5A-5B illustrate examples of a system 500 for commentarygeneration. FIG. 5A illustrates a user accessing a content item 506 of ahouse design article using a computing device 502. A conversationinterface 508 may be provided for the house design article so that usersviewing the house design article can discuss the house design article. Acomment generator 504 may be configured to automatically andprogrammatically monitor a conversation occurring within theconversation interface 508. The comment generator 504 may determine atone 512 of the conversation based upon comments 510 that are extractedfrom the conversation interface 508. The comments 510 are evaluated todetermine whether the comments 510 comprise negative conversationcomments, off topic conversation comments, and/or a lack of commentsindicative of a silent or uninteresting conversation. For example, thetone 512 may be indicative of a negative conversation tone that isdetermined based upon phrases “you are terrible”, “yuk”, “your poorclients”, and/or other inflammatory phrases within the comments 510 ofthe conversation. The tone 512 of a negative conversation tone maydeviate from a target tone 514 (e.g., an on topic tone, a tone of userengagement and discussion, a neutral or positive tone, a non-negativetone, etc.), and thus the comment generator 504 may intervene in theconversation in order to post one or more comments used to change thetone 512 to a more positive and/or interactive tone.

FIG. 5B illustrates the comment generator 504 extracting information 520such as a topic and/or body of the house design article. The commentgenerator 504 may determine subject matter of the house design articlebased upon the information 520. For example, words within the topicand/or the body of the house design article may correspond to stainlesssteel appliances. The comment generator 504 may generate 522 a naturallanguage statement from the subject matter of the house design article.For example, the natural language statement may comprise a question “doyou like stainless steel appliances?”. In an example, the commentgenerator 504 may search a structure language content source 526 toidentify structured knowledge associated with the house design article(e.g., a housing economic research article, indicating that stainlesssteel appliances provide a 90% return on investment, provided by aresearch website). The natural language statement may be generated 522to comprise an artificially generated fact from the structuredknowledge. For example, “stainless steel appliances provide a 90% returnon investment” may be combined with “do you like stainless steelappliances?” to create a comment 524 “do you like stainless steelappliances, I hear they have 90% return on investment?”. The commentgenerator 504 may post the comment 524 through the conversationinterface 508.

In an example, the comment 524 may be posted using a predefined name, arandomly generated name, or a determined name. For example, a female ora male name may be chosen based upon a gender composition of users ofthe conversation interface 508 (e.g., if all users are female and thecontent item deals with female clothing, then a female name may bechosen for posting the comment 524; if there is a mix of females andmales, then a female or male name may be picked to help maintain agender mix ratio such as a 50/50 ratio). In an example, a name may bedetermined that is distinct from names of other users of theconversation interface 508.

FIGS. 6A-6B illustrate examples of a system 600 for commentarygeneration. FIG. 6A illustrates a user accessing a content item 606 of afinancial advice live stream using a computing device 602. Aconversation interface 608 may be provided for the financial advice livestream so that users viewing the financial advice live stream candiscuss the financial advice live stream. A comment generator 604 may beconfigured to automatically monitor a conversation occurring within theconversation interface 608. The comment generator 604 may determine atone 612 of the conversation based upon comments 610 that are extractedfrom the conversation interface 608. The comments 610 are evaluated todetermine whether the comments 610 comprise negative conversationcomments, off topic conversation comments, and/or a lack of commentsindicative of a silent or uninteresting conversation. For example, thetone 612 may be indicative of a low participation conversation basedupon less than a threshold number of comments having been posted throughthe conversation interface 608 within a threshold timespan (e.g., a lastcomment may have been posted over 10 minutes ago). The tone 612 of a lowparticipation conversation tone may deviate from a target tone 614(e.g., an on topic tone, a tone of user engagement and discussion, aneutral or positive tone, a non-negative tone, etc.), and thus thecomment generator 604 may intervene in the conversation in order to postone or more comments used to change the tone 612 to a more positiveand/or interactive tone.

FIG. 6B illustrates the comment generator 604 extracting information 620such as a description of the financial advice live stream (e.g., thefinancial advice live stream may be associated with metadata and/orother text comprising a description of the financial advice livestream). The comment generator 604 may determine subject matter of thefinancial advice live stream based upon the information 620. Forexample, words within the description may correspond to stock marketinvesting advice. The comment generator 604 may query a content source,such as a financial resource content source 622, to identify targetcontent corresponding to the subject matter (e.g., forum posts within astock market history forum).

A snippet 624 (e.g., a statement posted within the stock market historyforum) may be extracted from the target content based upon a selectioncriteria 626. For example, the snippet 624 “historic stock market has areturn of 8%” may be selected based upon the snippet 624 being astatement that is contextually relevant to the financial advice livestream, is unique compared to other comments of the conversation, has ahigher probability to eliciting user engagement with the conversation,and/or mimics a natural language statement. In this way, a naturallanguage selection criteria, a uniqueness selection criteria, aresponsiveness selection criteria, and/or a contextual selectioncriteria may be used to assign weights to snippets within the stockmarket history forum so that a snippet having a desired weight (e.g., ahighest weight) is selected as the snippet 624.

The comment generator 604 may generate a comment 630 “I hear that thehistoric stock market return has been 8%, do you think this is true?”based upon the snippet 624 “historic stock market has a return of 8%”.For example, the snippet 624 may be modified by the comment generator604 into a question as the comment 630. The comment generator 624 maypost the comment 630 through the conversation interface 608.

FIGS. 7A-7B illustrate examples of a system 700 for commentarygeneration. FIG. 7A illustrates a user accessing a content item 706 of asports article using a computing device 702. A conversation interface708 may be provided for the sports article so that users viewing thesports article can discuss the sports article. A comment generator 704may be configured to automatically and programmatically monitor aconversation occurring within the conversation interface 708. Thecomment generator 704 may determine a tone 712 of the conversation basedupon comments 710 that are extracted from the conversation interface708. The comments 710 are evaluated to determine whether the comments710 comprise negative conversation comments, off topic conversationcomments, and/or a lack of comments indicative of a silent oruninteresting conversation. For example, the tone 712 may be indicativeof an off topic conversation tone where the conversation went fromdiscussing a football game aspect of the sports article to a negativevideogame console war argument that does not relate to the sportsarticle. The tone 712 of the off topic conversation tone may deviatefrom a target tone 714 (e.g., an on topic tone, a tone of userengagement and discussion, a neutral or positive tone, a non-negativetone, etc.), and thus the comment generator 704 may intervene in theconversation in order to post one or more comments used to change thetone 712 to a more positive and/or on topic tone.

FIG. 7B illustrates the comment generator 704 extracting information 720such as a topic and/or body of the sports article. The comment generator704 may determine subject matter of the sports article based upon theinformation 720. For example, words within the topic and/or the body ofthe sports article may correspond to a recent football game wherefootball player (X) made an almost impossible touchdown, and thus thesubject matter may correspond to the recent football game, the footballplayer (X), and the touchdown play. The comment generator 704 mayutilize a comment generator model 722 (e.g., a neural network trained togenerate text that is topical for subject matter of content items, suchas text that is topical for the recent football game, the footballplayer (X), and/or the touchdown play) to construct a comment 724 basedupon the subject matter. For example, the comment 724 “I watched thefootball game, I liked that one play . . . ” may be topical for therecent football game, the football player (X), and/or the touchdownplay. The comment generator 704 may post the comment 724 through theconversation interface 708.

FIG. 8 is an illustration of a scenario 800 involving an examplenon-transitory machine readable medium 802. The non-transitory machinereadable medium 802 may comprise processor-executable instructions 812that when executed by a processor 816 cause performance (e.g., by theprocessor 816) of at least some of the provisions herein. Thenon-transitory machine readable medium 802 may comprise a memorysemiconductor (e.g., a semiconductor utilizing static random accessmemory (SRAM), dynamic random access memory (DRAM), and/or synchronousdynamic random access memory (SDRAM) technologies), a platter of a harddisk drive, a flash memory device, or a magnetic or optical disc (suchas a compact disk (CD), a digital versatile disk (DVD), or floppy disk).The example non-transitory machine readable medium 802 storescomputer-readable data 804 that, when subjected to reading 806 by areader 810 of a device 808 (e.g., a read head of a hard disk drive, or aread operation invoked on a solid-state storage device), express theprocessor-executable instructions 812. In some embodiments, theprocessor-executable instructions 812, when executed cause performanceof operations, such as at least some of the example method 400 of FIG.4, for example. In some embodiments, the processor-executableinstructions 812 are configured to cause implementation of a system,such as at least some of the example system 500 of FIGS. 5A-5B, at leastsome of the example system 600 of FIGS. 6A-6B, and/or at least some ofthe example system 700 of FIGS. 7A-7B, for example.

3. Usage of Terms

As used in this application, “component,” “module,” “system”,“interface”, and/or the like are generally intended to refer to acomputer-related entity, either hardware, a combination of hardware andsoftware, software, or software in execution. For example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution, a program,and/or a computer. By way of illustration, both an application runningon a controller and the controller can be a component. One or morecomponents may reside within a process and/or thread of execution and acomponent may be localized on one computer and/or distributed betweentwo or more computers.

Unless specified otherwise, “first,” “second,” and/or the like are notintended to imply a temporal aspect, a spatial aspect, an ordering, etc.Rather, such terms are merely used as identifiers, names, etc. forfeatures, elements, items, etc. For example, a first object and a secondobject generally correspond to object A and object B or two different ortwo identical objects or the same object.

Moreover, “example” is used herein to mean serving as an example,instance, illustration, etc., and not necessarily as advantageous. Asused herein, “or” is intended to mean an inclusive “or” rather than anexclusive “or”. In addition, “a” and “an” as used in this applicationare generally be construed to mean “one or more” unless specifiedotherwise or clear from context to be directed to a singular form. Also,at least one of A and B and/or the like generally means A or B or both Aand B. Furthermore, to the extent that “includes”, “having”, “has”,“with”, and/or variants thereof are used in either the detaileddescription or the claims, such terms are intended to be inclusive in amanner similar to the term “comprising”.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing at least some of the claims.

Furthermore, the claimed subject matter may be implemented as a method,apparatus, or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device, carrier, or media. Of course, manymodifications may be made to this configuration without departing fromthe scope or spirit of the claimed subject matter.

Various operations of embodiments are provided herein. In an embodiment,one or more of the operations described may constitute computer readableinstructions stored on one or more computer readable media, which ifexecuted by a computing device, will cause the computing device toperform the operations described. The order in which some or all of theoperations are described should not be construed as to imply that theseoperations are necessarily order dependent. Alternative ordering will beappreciated by one skilled in the art having the benefit of thisdescription. Further, it will be understood that not all operations arenecessarily present in each embodiment provided herein. Also, it will beunderstood that not all operations are necessary in some embodiments.

Also, although the disclosure has been shown and described with respectto one or more implementations, equivalent alterations and modificationswill occur to others skilled in the art based upon a reading andunderstanding of this specification and the annexed drawings. Thedisclosure includes all such modifications and alterations and islimited only by the scope of the following claims. In particular regardto the various functions performed by the above described components(e.g., elements, resources, etc.), the terms used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (e.g., that is functionally equivalent), even though notstructurally equivalent to the disclosed structure. In addition, while aparticular feature of the disclosure may have been disclosed withrespect to only one of several implementations, such feature may becombined with one or more other features of the other implementations asmay be desired and advantageous for any given or particular application.

What is claimed is:
 1. A method of message classification, the methodcomprising: executing, on a processor of a computing device,instructions that cause the computing device to perform operations, theoperations comprising: monitoring a conversation occurring through aconversation interface, comprising a chat interface, associated with acontent item to identify a tone of the conversation based upon commentssubmitted by users through the conversation interface, wherein theidentifying the tone is based upon at least one of (i) determining anumber of comments, of the comments submitted by the users through theconversation interface, that have inflammatory content or (ii)determining a number of the comments submitted by the users through theconversation interface; and responsive to determining that the tone doesnot correspond to a target tone associated with a threshold and insteaddeviates from the target tone based upon at least one of (i) adetermination that the number of the comments that have inflammatorycontent exceeds a threshold number of comments or (ii) a determinationthat the number of the comments submitted by the users through theconversation interface is indicative of a lack of comments: identifyinga subject matter of the content item for which the conversationinterface is associated; generating a natural language statement basedupon the subject matter; and posting the natural language statement as acomment through the conversation interface.
 2. The method of claim 1,wherein the generating comprises: utilizing one or more words of a topicof the content item to generate a question as the natural languagestatement.
 3. The method of claim 1, wherein the generating comprises:utilizing one or more words of a body of the content item to generate aquestion as the natural language statement.
 4. The method of claim 1,wherein the generating comprises: searching a structured knowledgecontent source using the subject matter of the content item to identifystructured knowledge associated with the content item; and generatingthe natural language statement to comprise an artificially generatedfact derived from the structured knowledge.
 5. The method of claim 1,wherein the operations comprise: determining that the tone deviates fromthe target tone based upon the tone corresponding to at least one of anegative conversation tone, an off topic conversation tone, or a silentconversation tone.
 6. A computing device comprising: a processor; andmemory comprising processor-executable instructions that when executedby the processor cause performance of operations, the operationscomprising: monitoring a conversation occurring through a conversationinterface, comprising a chat interface, associated with a content itemto identify a tone of the conversation based upon comments submitted byusers through the conversation interface, wherein the identifying thetone is based upon determining a number of comments, of the commentssubmitted by the users through the conversation interface, that haveinflammatory content; and responsive to determining that the tonedeviates from a target tone associated with a threshold based upon adetermination that the number of the comments that have inflammatorycontent exceeds a threshold number of comments: identifying a subjectmatter of the content item for which the conversation is associated;querying a content source to identify target content corresponding tothe subject matter; extracting a snippet from the target content basedupon a selection criteria; generating a comment based upon the snippet;and posting the comment through the conversation interface.
 7. Thecomputing device of claim 6, wherein the generating a comment comprises:utilizing the snippet as a quote from the target content; and includingthe quote and a citation to the target content within the comment. 8.The computing device of claim 6, wherein the selection criteriacomprises a natural language selection criteria used to assign weightsto snippets based upon how much the snippets correspond to naturallanguage statements.
 9. The computing device of claim 6, wherein theselection criteria comprises a uniqueness selection criteria used toassign weights to snippets based upon how unique content of the snippetsis from comments of the conversation.
 10. The computing device of claim6, wherein the selection criteria comprises a responsiveness selectioncriteria used to assign weights to snippets based upon probabilitiesthat snippets will elicit users to submit reply comments to thesnippets.
 11. The computing device of claim 6, wherein the selectioncriteria comprises a contextual selection criteria used to assignweights to snippets based upon contextual relevancy of the snippets tothe content item.
 12. A non-transitory machine readable medium havingstored thereon processor-executable instructions that when executedcause performance of operations, the operations comprising: monitoring aconversation occurring through a conversation interface, comprising achat interface, associated with a content item to identify a tone ofconversation-based comments submitted by users through the conversationinterface, wherein the identifying the tone is based upon determining anumber of comments submitted by the users through the conversationinterface; and responsive to determining that the tone deviates from atarget tone associated with a threshold based upon a determination thatthe number of the comments submitted by the users through theconversation interface is indicative of a lack of comments: identifyinga subject matter of the content item for which the conversationinterface is associated; utilizing a comment generator model toconstruct a comment based upon the subject matter; and posting thecomment through the conversation interface.
 13. The non-transitorymachine readable medium of claim 12, wherein the comment generator modelcomprises a neural network configured to generate text that is topicalfor the subject matter.
 14. The non-transitory machine readable mediumof claim 13, wherein the operations comprise: training the neuralnetwork based upon comments that are labeled as positive comments. 15.The non-transitory machine readable medium of claim 14, wherein theoperations comprise: labeling a training comment as a positive commentbased upon a threshold number of approval ratings being assigned to thetraining comment by users.
 16. The non-transitory machine readablemedium of claim 12, wherein the operations comprise: generating acontext vector for the content item based upon the subject matter,wherein the context vector comprises words extracted from the contentitem; and utilizing the comment generator model to construct the commentbased upon the context vector.
 17. The non-transitory machine readablemedium of claim 12, wherein the operations comprise: utilizing thecomment generator model to construct a question as the comment.
 18. Thenon-transitory machine readable medium of claim 12, wherein theoperations comprise: generating a matrix of user interests associatedwith users of the conversation interface; and utilizing a factorizationmachine, of the comment generator model, to generate the comment basedupon the matrix of user interests.
 19. The non-transitory machinereadable medium of claim 12, wherein the operations comprise: utilizingthe comment generator model to generate the comment based upon contentof a user comment by a user that has left the conversation interface.20. The non-transitory machine readable medium of claim 12, wherein theoperations comprise: utilizing a user comment of a user, useridentifying information of the user, and co-commentary behavior of userswith the user to predict a likelihood of the user re-engaging in theconversation based upon a potential comment generated by the commentgenerator model; and responsive to the likelihood exceeding a threshold,posting the potential comment as the comment within the conversationinterface.